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Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control
Citation Link: https://doi.org/10.15480/882.2267
Publikationstyp
Journal Article
Date Issued
2019-05-06
Sprache
English
Author(s)
Institut
TORE-DOI
TORE-URI
Journal
Volume
19
Issue
9
Start Page
Art.-Nr. 2094
Citation
Sensors 19 (9): 2094 (2019)
Publisher DOI
Scopus ID
Publisher
Multidisciplinary Digital Publishing Institute
Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.
Subjects
autonomous exploration
environmental field monitoring
Gaussian processes
Gaussian Markov random fields
Kalman filtering
stochastic optimal control
DDC Class
510: Mathematik
600: Technik
620: Ingenieurwissenschaften
Funding(s)
More Funding Information
Deutsche Forschungsgemeinschaft (DFG)
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Name
sensors-19-02094.pdf
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2.67 MB
Format
Adobe PDF